Attribute-based person tracking across multiple cameras

ABSTRACT

Techniques for tracking an individual across two or more cameras are provided. The techniques include detecting an image of one or more individuals in each of two or more cameras, tracking each of the one or more individuals in a field of view in each of the two or more cameras, applying a set of one or more attribute detectors to each of the one or more individuals being tracked by the two or more cameras, and using the set of one or more attribute detectors to match an individual tracked in one of the two or more cameras with an individual tracked in one or more other cameras of the two or more cameras.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to U.S. patent application entitled“Multispectral Detection of Personal Attributes for Video Surveillance,”identified by Ser. No. 12/845,121 and filed concurrently herewith, thedisclosure of which is incorporated by reference herein in its entirety.

Additionally, the present application is related to U.S. patentapplication entitled “Facilitating People Search in Video Surveillance,”identified by Ser. No. 12/845,116, and filed concurrently herewith, thedisclosure of which is incorporated by reference herein in its entirety.

Also, the present application is related to U.S. patent applicationentitled “Semantic Parsing of Objects in Video,” identified by Ser. No.12/845,095, and filed concurrently herewith, the disclosure of which isincorporated by reference herein in its entirety.

FIELD OF THE INVENTION

Embodiments of the invention generally relate to information technology,and, more particularly, to video surveillance.

BACKGROUND OF THE INVENTION

Tracking people across multiple cameras with non-overlapping fields ofview poses a challenge. Existing approaches include using facerecognition technology or relying on soft-biometrics features such asclothing color or person height to identify and track a person overdifferent camera views. However, face recognition techniques are verysensitive to illumination changes, face pose variations, andlow-resolution imagery (typical conditions in surveillance scenes).Also, general features like clothing color are subject to ambiguity (forexample, two different people may be wearing clothes with the samecolor). Additionally, color and appearance of a person can changedramatically from camera to camera, due to lighting changes, differentcamera sensor responses, etc.

SUMMARY OF THE INVENTION

Principles and embodiments of the invention provide techniques forattribute-based person tracking across multiple cameras. An exemplarymethod (which may be computer-implemented) for tracking an individualacross two or more cameras, according to one aspect of the invention,can include steps of detecting an image of one or more individuals ineach of two or more cameras, tracking each of the one or moreindividuals in a field of view in each of the two or more cameras,applying a set of one or more attribute detectors to each of the one ormore individuals being tracked by the two or more cameras, and using theset of one or more attribute detectors to match an individual tracked inone of the two or more cameras with an individual tracked in one or moreother cameras of the two or more cameras.

One or more embodiments of the invention or elements thereof can beimplemented in the form of a computer product including a tangiblecomputer readable storage medium with computer useable program code forperforming the method steps indicated. Furthermore, one or moreembodiments of the invention or elements thereof can be implemented inthe form of an apparatus including a memory and at least one processorthat is coupled to the memory and operative to perform exemplary methodsteps.

Yet further, in another aspect, one or more embodiments of the inventionor elements thereof can be implemented in the form of means for carryingout one or more of the method steps described herein; the means caninclude (i) hardware module(s), (ii) software module(s), or (iii) acombination of hardware and software modules; any of (i)-(iii) implementthe specific techniques set forth herein, and the software modules arestored in a tangible computer-readable storage medium (or multiple suchmedia).

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary embodiment,according to an aspect of the invention;

FIG. 2 is a flow diagram illustrating techniques for tracking anindividual across two or more cameras, according to an embodiment of theinvention; and

FIG. 3 is a system diagram of an exemplary computer system on which atleast one embodiment of the invention can be implemented.

DETAILED DESCRIPTION OF EMBODIMENTS

Principles of the invention include attribute-based people trackingacross multiple cameras (for example, video cameras). One or moreembodiments of the invention can be used, for example, for smart videosurveillance to note trajectories for each of multiple people in athree-dimensional (3D) space. This can be useful, by way of example, fortracking individuals across cameras, observing common shopper patternsin retail stores, etc.

As described herein, one or more embodiments of the invention canaddress the issue of, for example, given a tracked person in camera A,and a person in camera B, determining whether they correspond to thesame person (so that the person can be unified into a single videotrack). As such, one or more embodiments of the invention includetracking people across multiple cameras based on fine-grained body partsand attribute detectors.

As detailed herein, images of people can be matched based on a set offine-grained attributes such as, for example, the presence of beard,moustache, eyeglasses, sunglasses, hat, absence of hair (bald people),shape of features (long nose, eyes shape, short/long shirt sleeves),color and texture of clothing, etc. These attribute detectors arelearned from large amounts of training images, in multiple levels ofresolution. Further, one or more embodiments of the invention can alsoconsider non-visual attributes from other sensors such as, for example,odor and temperature to improve the matching process.

By way of example and not limitation, consider a set of surveillancecameras with non-overlapping fields of view. In each camera, people aredetected and tracked in the local field of view using standard computervision algorithms. Now suppose a person moves from a place that ismonitored by camera A to another place that is monitored by camera B.One or more embodiments of the invention include assigning a unique“track ID” (track identifier) for the trajectories of the person locallytracked in cameras A and B, so that tracking in a global coordinatesystem can be performed. In one or more embodiments of the invention, a“track ID” might simply include a number to identify the track, such as,for example, track ID 1, track ID 2, etc.

This implies a need to match a person tracked in one camera with otherpeople tracked in other cameras. In one or more embodiments of theinvention, this matching can be performed by taking into account thegeometric configuration of the cameras, the time information (that is,the time usually taken by a person to walk from camera A to B), and aset of fine-grained person attributes as described herein.

One or more embodiments of the invention can be implemented inconnection with a set of surveillance cameras with non-overlappingfields of view. As described herein, a unique track identifier (ID) canbe generated and used for the trajectories of a person locally trackedin cameras. For each person that enters the scene, a “track” is started.The “track” ends when the person leaves the scene (that is, leaves thecamera field of view). Each track has a unique track ID to identify thetrack. Also, by way of example, this can be generatedincrementally—track 1, track 2, . . . , track n.

Further, one or more embodiments of the invention use a feature formatching a person tracked in one camera with other people (for example,the same person) tracked on other cameras using a set of fine-grainedperson attributes. Additionally, as further detailed herein, a techniqueand/or algorithm for matching people across cameras based on a set offine-grained parts and attributes detectors is used, wherein thedetectors are learned from large amount of training data (for example,using Adaptive Boosting (Adaboost) learning, thus being robust tolighting and viewpoint changes.

In one or more embodiments of the invention, as noted herein, matchingpeople across cameras can also include a matching algorithm using thegeometric configuration of the cameras (including time information) anda set of fine-grained person attributes. Also, one or more embodimentsof the invention can include using a human parsing process and/or amethodology applying for both tracking and human parsing. Further, aweighted vector distance matrix and a threshold can be used, forexample, in conjunction with a comparison method to determine if theperson in camera A corresponds to the person in camera B.

By way of example and not limitation, consider again a person movingfrom camera A to camera B. One or more embodiments of the inventioncould proceed as follows. The person is tracked in camera A, forexample, using standard tracking techniques. In addition, a set offine-grained Adaboost detectors (including, for example, detectors forbeard, glasses, hat, baldness, etc.) is applied to each image of theperson. By way of example only, techniques for generating such detectorscan be found in U.S. patent application Ser. No. 12/845,119 entitled“Multispectral Detection of Personal Attributes for Video Surveillance,”filed concurrently herewith, the disclosure of which is incorporated byreference herein in its entirety. This process can be referred to hereinas human parsing.

Using the human parsing process, one or more embodiments of theinvention can include obtaining a feature vector F_(a)=[a₁, u₂, a₃, . .. , a_(n)] for the person in camera A, corresponding to the maxconfidence values of each Adaboost detector. Note that each “track”corresponds to a set of image frames with the location of the person ineach frame. For each frame, one or more embodiments of the inventionapply the set of attribute detectors (beard, eyeglasses, etc.) andobtain a confidence value for each detector in each frame. As such, eachdetector will have a set of confidence values (one for each frame of thetrack). For each detector, one or more embodiments of the invention takethe maximum confidence value over the frames (that is, “max confidencevalue”) and store it as one element in the F_(a) vector. Accordingly,the first element of the F_(a) vector will contain the max confidencevalue, for example, for beard, the second element, for example, foreyeglasses, and so on.

By way of example and not limitation, consider that a person has beentracked over five frames. All detectors are applied in each one of thesefive frames, generating confidence values. For example, the bearddetector may generate five confidence values [0.2, 0.5, 0.9, 0.2, 0.7].The “max confidence value” for the beard attribute is therefore 0.9 (themaximum value of the five confidence numbers).

The same methodology of tracking and human parsing can be applied whenthe person is moving within the field of view of camera B, resulting inone or more embodiments of the invention obtaining the feature vectorF_(b)=[b₁, b₃ . . . , b_(n)].

One or more embodiments of the invention can additionally determine theamount of time taken by the person to leave camera A and appear incamera B (given the geometric arrangement of cameras) to see if it isconsistent with an average or customary time calculated previously basedon earlier data collected, for example, by camera A and camera B. One ormore embodiments of the invention include specifying a range of timeperiods calculated based on earlier collected data, as well as defininga priori if it is possible for one person to move from one camera toanother based on the spatial location of cameras.

If the amount of time is consistent, one or more embodiments of theinvention can include computing a weighted vector distance between F_(a)and F_(b):

$\sum\limits_{i = 1}^{n}{w_{i}\left( {a_{i} - b_{i}} \right)}^{2}$and comparing the weighted vector distance to a threshold to see if theperson in camera A corresponds to person in camera B. Both the thresholdand the set of weights can be obtained from a standard learning process(for example, using artificial neural networks). The learning processcan be seen as a black box where training data is provided (includinginput feature vectors and an output variable (indicating whether it isthe same person or not). The output of the black box can include weightsand the threshold. If there is a match (that is, the person in camera Acorresponds to the person in camera B), then both trajectories areunified so that they correspond to a single track.

Also, as noted herein, in one or more embodiments of the invention, theweights w_(i) are obtained offline according to the reliability of eachdetector. As an example, if a “beard detector” is more reliable than a“hat detector” then the beard detector confidence value will beassociated with a larger weight.

FIG. 1 is a block diagram illustrating an exemplary embodiment (for anypair of cameras A and B), according to an aspect of the invention. FIG.1 depicts camera A 102 which captures images, leading to the steps ofperson detection 104 (via a person detection module), person tracking106 (via a person tracking module) and human parsing 108 (via use offine-grained attribute (for example, beard, eyeglasses, hat, etc.)detector modules). This sequence produces feature vector 1 inconjunction with the use of detector confidence values.

As detailed herein, the task of a person detection module includes,given a specific video frame, finding whether there exists such a personas is being sought and, if so, determining his/her size and position inthe image. The task of a person tracking module includes, given that aperson was detected at frame N, localizing the same person at frame N+1,N+2, etc. until the person leaves the scene. Also, in one or moreembodiments of the invention, a feature vector is created by the humanparsing process via applying attribute detectors such as, for example,beard, etc. and taking the max confidence values as explained herein.

Additionally, FIG. 1 depicts camera B 110 which captures images, leadingto the steps of person detection 112 (via a person detection module),person tracking 114 (via a person tracking module) and human parsing 116(via use of fine-grained attribute (for example, beard, eyeglasses, hat,etc.) detector modules). This sequence produces feature vector 2 inconjunction with the use of detector confidence values.

FIG. 1 also depicts step 118, which includes use of a configuration andtiming analysis module to determine if a person can move from camera Ato camera B in a given time. Step 120 includes determining if theconfiguration and timing of the person being surveilled is consistentwith the pre-determined range. In one or more embodiments of theinvention, the user estimates the minimum and maximum times that aperson could walk from camera A to Camera B. Also, in one or moreembodiments of the invention, it is assumed that the person does notstop for a long time and keeps walking or running.

If the configuration and timing of the person being surveilled is notconsistent with the pre-determined range, the process ends at step 130.If the configuration and timing of the person being surveilled isconsistent with the pre-determined range, step 122 includes comparingfeature vector 1 with feature vector 2 via use of a matching module aswell as receiving input from a learning module 124 in the form ofdeterminations of weights and thresholds for matching. Step 126 includesdetermining if the matching of feature vector 1 and feature vector 2 isacceptable. If no, the process ends at step 130. If yes, the tracks areunified at step 128, indicating that the same person is being compared(that is, track 1=track 2). Additionally, in one or more embodiments ofthe invention, the matching, that is, the distance between the twofeature vectors is compared to a user-defined threshold to determinewhether the persons are the same or not.

FIG. 2 is a flow diagram illustrating techniques for tracking anindividual across two or more cameras, according to an embodiment of thepresent invention. Step 202 includes detecting an image of one or moreindividuals in each of two or more cameras. This step can be carriedout, for example, using a person detection module.

Step 204 includes tracking each of the one or more individuals in a (forexample, a local) field of view in each of the two or more cameras. Thisstep can be carried out, for example, using a person tracking module.Tracking each of the individuals in a local field of view in each of thetwo or more cameras can include using one or more computer visionalgorithms.

Step 206 includes applying a set of one or more attribute detectors toeach of the one or more individuals being tracked by the two or morecameras. This step can be carried out, for example, using a humanparsing module. The set of attribute detectors can include, for example,a set of one or more fine-grained Adaptive Boosting attribute detectors.Further, the attribute detectors can include one or more attributedetectors for presence of a beard, presence of a moustache, presence ofeyeglasses, presence of sunglasses, presence of a hat, absence of hair,shape of body features, shape of clothing features, clothing color,clothing texture, etc. Also, the attribute detectors can be learned froma set of training images in one or more levels of resolution.

Step 208 includes using the set of one or more attribute detectors tomatch an individual tracked in one of the two or more cameras with anindividual tracked in one or more other cameras of the two or morecameras. This step can be carried out, for example, using a matchingmodule. Using the set of attribute detectors to match an individualtracked in one of the cameras with an individual tracked in one or moreother cameras can include, for example, using a geometric configurationof the cameras, time information, and/or a set of individual attributes.

Using the set of one or more attribute detectors to match an individualtracked in one of the two or more cameras with an individual tracked inone or more other cameras of the two or more cameras can also includesteps 210, 212 and 214. Step 210 includes using a maximum confidencevalue of each attribute detector to generate a feature vector of each ofthe one or more individuals. Step 212 includes calculating a distancebetween each vector using a weighted vector distance between eachvector. Also, step 214 includes comparing the distance to a threshold todetermine if the individual tracked in one of the two or more cameras isthe same individual as the individual tracked in one or more othercameras of the two or more cameras.

In one or more embodiments of the invention, the geometric configurationcan be acquired before the system starts. By way of example and notlimitation, consider Camera A, and assume that a person can walk fromcamera A to Camera B, but cannot to walk from camera A to Camera C (forexample, because there is a physical wall, etc.). The geometricconfiguration will contain this information, indicating which camerasare adjacent.

Additionally, with respect to the matching process, consider (forillustrative purposes) the following example. A person shows up incamera A. He of she is detected, tracked, and the attribute detectorsare applied. By taking the max confidence values of each detector, afeature vector Fa is formed. Assume, also, that there are fiveattributes (for example, beard, bald, eyeglasses, hat, and stripedshirt). Accordingly, the feature vector Fa is 5-dimensional, forexample, Fa=[0.4, 0.9, 0.0, 0.9, 0.2]. Subsequently, another personshows up in camera B. The same process is done and a second featurevector is obtained, for example, Fb=[0.2, 1.0, 0.0, 0.1, 0.1]. Thedistance between the two vectors (that is, matching) is calculated, asdescribed herein, using a

$\sum\limits_{i = 1}^{n}{w_{i}\left( {a_{i} - b_{i}} \right)}^{2}$weighted vector distance between F_(a) and F_(b): and comparing that toa threshold to determine if the persons are the same or not.

The techniques depicted in FIG. 2 can also include assigning a uniquetracking identifier (“track ID”) for each trajectory of an individuallocally tracked in each of the two or more cameras to facilitatetracking in a global coordinate system. Additionally, one or moreembodiments of the invention can include obtaining a feature vector(F_(a)=[a₁, a₂, a₃, . . . , a_(n)]) for an individual in one camera,corresponding to one or more max confidence values of each attributedetector, as well as obtaining a feature vector (F_(b)=[b₁, b₂, b₃, . .. , b_(n)]) for the individual when the individual moves within thefield of view of a second camera. As detailed herein, examples ofattributes include beard, bald, etc., and a feature vector is formed bytaking the max confidence values of the attribute detectors.

Also, one or more embodiments of the invention include determining anamount of time taken by the individual to leave a view of the firstcamera and appear within a view of the second camera to determine if theamount of time is consistent with an established range of time. Further,one or more embodiments of the invention can include computing, if theamount of time is consistent with the established range time, a weightedvector distance between the first camera and the second camera

$\left( {\sum\limits_{i = 1}^{n}{w_{i}\left( {a_{i} - b_{i}} \right)}^{2}} \right)$and comparing the weighted vector distance to a threshold to determineif the individual in the view of the first camera corresponds to theindividual in the view of the second camera.

A trajectory of the first camera and the second camera can be unified tocorrespond to a single track if the individual in the view of the firstcamera corresponds to the individual in the view of the second camera.In one or more embodiments of the invention, a trajectory refers to thepath take by a person. When tracking the person in one camera, it can bedetermined, for example, that the person is moving right or left.Accordingly, his/her path is known within the field of view of a camera.In another camera, the person may follow another trajectory. When it isdetermined that the same person is present in different cameras views,the trajectories are linked. A single track, as used herein, refers to asingle trajectory, rather than two different trajectories. One or moreembodiments of the invention utilize this information to discover, forexample, the path that a person-of-interest took, where he/she went,etc. Additionally, one or more weights used in the weighted vector canbe obtained according to the reliability of each attribute detector. Thereliability of each attribute detector can be determined using anartificial neural network (as well as, for example, other standardmachine learning techniques).

The techniques depicted in FIG. 2 can also include using one or morenon-visual attributes from one or more sensors. Non-visual attributescan include, by way of example and not limitation, odor, temperature,etc. In one or more embodiments of the invention, the use of non-visualattribute detectors can include generating confidence values, just aswith the visual attribute detectors.

The techniques depicted in FIG. 2 can also, as described herein, includeproviding a system, wherein the system includes distinct softwaremodules, each of the distinct software modules being embodied on atangible computer-readable recordable storage medium. All the modules(or any subset thereof) can be on the same medium, or each can be on adifferent medium, for example. The modules can include any or all of thecomponents shown in the figures. In one or more embodiments, the modulesinclude a person detection module, a person tracking module, a humanparsing module, a matching module, a configuration and timing analysismodule, and a learning module that can run, for example on one or morehardware processors.

The method steps can then be carried out using the distinct softwaremodules of the system, as described above, executing on the one or morehardware processors. Further, a computer program product can include atangible computer-readable recordable storage medium with code adaptedto be executed to carry out one or more method steps described herein,including the provision of the system with the distinct softwaremodules.

Additionally, the techniques depicted in FIG. 2 can be implemented via acomputer program product that can include computer useable program codethat is stored in a computer readable storage medium in a dataprocessing system, and wherein the computer useable program code wasdownloaded over a network from a remote data processing system. Also, inone or more embodiments of the invention, the computer program productcan include computer useable program code that is stored in a computerreadable storage medium in a server data processing system, and whereinthe computer useable program code are downloaded over a network to aremote data processing system for use in a computer readable storagemedium with the remote system.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

One or more embodiments of the invention, or elements thereof, can beimplemented in the form of an apparatus including a memory and at leastone processor that is coupled to the memory and operative to performexemplary method steps.

One or more embodiments can make use of software running on a generalpurpose computer or workstation. With reference to FIG. 3, such animplementation might employ, for example, a processor 302, a memory 304,and an input/output interface formed, for example, by a display 306 anda keyboard 308. The term “processor” as used herein is intended toinclude any processing device, such as, for example, one that includes aCPU (central processing unit) and/or other forms of processingcircuitry. Further, the term “processor” may refer to more than oneindividual processor. The term “memory” is intended to include memoryassociated with a processor or CPU, such as, for example, RAM (randomaccess memory), ROM (read only memory), a fixed memory device (forexample, hard drive), a removable memory device (for example, diskette),a flash memory and the like. In addition, the phrase “input/outputinterface” as used herein, is intended to include, for example, one ormore mechanisms for inputting data to the processing unit (for example,mouse), and one or more mechanisms for providing results associated withthe processing unit (for example, printer). The processor 302, memory304, and input/output interface such as display 306 and keyboard 308 canbe interconnected, for example, via bus 310 as part of a data processingunit 312. Suitable interconnections, for example via bus 310, can alsobe provided to a network interface 314, such as a network card, whichcan be provided to interface with a computer network, and to a mediainterface 316, such as a diskette or CD-ROM drive, which can be providedto interface with media 318.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in one or more of the associated memory devices (for example,ROM, fixed or removable memory) and, when ready to be utilized, loadedin part or in whole (for example, into RAM) and implemented by a CPU.Such software could include, but is not limited to, firmware, residentsoftware, microcode, and the like.

A data processing system suitable for storing and/or executing programcode will include at least one processor 302 coupled directly orindirectly to memory elements 304 through a system bus 310. The memoryelements can include local memory employed during actual implementationof the program code, bulk storage, and cache memories which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringimplementation.

Input/output or I/O devices (including but not limited to keyboards 308,displays 306, pointing devices, and the like) can be coupled to thesystem either directly (such as via bus 310) or through intervening I/Ocontrollers (omitted for clarity).

Network adapters such as network interface 314 may also be coupled tothe system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Modems, cable modem andEthernet cards are just a few of the currently available types ofnetwork adapters.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 312 as shown in FIG. 3)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

As noted, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon. Anycombination of one or more computer readable medium(s) may be utilized.The computer readable medium may be a computer readable signal medium ora computer readable storage medium. A computer readable storage mediummay be, for example, but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device, or any suitable combination of the foregoing. Media block 318is a non-limiting example. More specific examples (a non-exhaustivelist) of the computer readable storage medium would include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, a portable compact disc read-onlymemory (CD-ROM), an optical storage device, a magnetic storage device,or any suitable combination of the foregoing. In the context of thisdocument, a computer readable storage medium may be any tangible mediumthat can contain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, radio frequency (RF), etc., or anysuitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, component, segment,or portion of code, which comprises one or more executable instructionsfor implementing the specified logical function(s). It should also benoted that, in some alternative implementations, the functions noted inthe block may occur out of the order noted in the figures. For example,two blocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the components shown in FIG. 1. Themethod steps can then be carried out using the distinct software modulesand/or sub-modules of the system, as described above, executing on oneor more hardware processors 302. Further, a computer program product caninclude a computer-readable storage medium with code adapted to beimplemented to carry out one or more method steps described herein,including the provision of the system with the distinct softwaremodules.

In any case, it should be understood that the components illustratedherein may be implemented in various forms of hardware, software, orcombinations thereof; for example, application specific integratedcircuit(s) (ASICS), functional circuitry, one or more appropriatelyprogrammed general purpose digital computers with associated memory, andthe like. Given the teachings of the invention provided herein, one ofordinary skill in the related art will be able to contemplate otherimplementations of the components of the invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

At least one embodiment of the invention may provide one or morebeneficial effects, such as, for example, tracking people, in videosurveillance, across multiple cameras based on fine-grained body partsand attribute detectors.

It will be appreciated and should be understood that the exemplaryembodiments of the invention described above can be implemented in anumber of different fashions. Given the teachings of the inventionprovided herein, one of ordinary skill in the related art will be ableto contemplate other implementations of the invention. Indeed, althoughillustrative embodiments of the present invention have been describedherein with reference to the accompanying drawings, it is to beunderstood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may bemade by one skilled in the art.

What is claimed is:
 1. A method for tracking an individual across two ormore cameras, wherein the method comprises: detecting an image of one ormore individuals in each of two or more cameras; tracking each of theone or more individuals in a field of view in each of the two or morecameras; applying a set of multiple attribute detectors to the images ofeach of the one or more individuals being tracked by the two or morecameras, wherein said attribute detectors (i) provide a probability thatgiven attributes are present in an image and (ii) are learned fromtraining images in multiple levels of resolution to produce robustnessto multiple changes in lighting and viewpoint; and using the set ofmultiple attribute detectors to match an individual tracked in one ofthe two or more cameras with an individual tracked in one or more othercameras of the two or more cameras, wherein using the set of multipleattribute detectors to match an individual tracked in one of the two ormore cameras with an individual tracked in one or more other cameras ofthe two or more cameras comprises: using a maximum confidence value ofeach of the multiple attribute detectors to generate a feature vector ofeach of the one or more individuals; determining a weight associatedwith each of the multiple attribute detectors, wherein said weightcorresponds to a reliability measure of the corresponding attributedetector relative to the remaining attribute detectors in the set ofmultiple attribute detectors; calculating a distance between each vectorusing a weighted vector distance between each vector based on thedetermined weight associated with each of the multiple attributedetectors; and comparing the distance to a threshold to determine if theindividual tracked in one of the two or more cameras is the sameindividual as the individual tracked in one or more other cameras of thetwo or more cameras.
 2. The method of claim 1, further comprisingassigning a unique tracking identifier for each trajectory of anindividual locally tracked in each of the two or more cameras tofacilitate tracking in a global coordinate system.
 3. The method ofclaim 1, further comprising obtaining a feature vector for theindividual when the individual moves within the field of view of asecond camera.
 4. The method of claim 3, further comprising determiningan amount of time taken by the individual to leave a view of the firstcamera and appear within a view of the second camera to determine if theamount of time is consistent with an established range of time.
 5. Themethod of claim 4, further comprising computing, if the amount of timeis consistent with the established range time, a weighted vectordistance between the first camera and the second camera and comparingthe weighted vector distance to a threshold to determine if theindividual in the view of the first camera corresponds to the individualin the view of the second camera.
 6. The method of claim 5, furthercomprising unifying a trajectory of the first camera and the secondcamera to correspond to a single track if the individual in the view ofthe first camera corresponds to the individual in the view of the secondcamera.
 7. The method of claim 1, wherein the reliability of each of themultiple attribute detectors is determined using an artificial neuralnetwork.
 8. The method of claim 1, wherein the set of multiple attributedetectors comprises a set of multiple fine-grained Adaptive Boostingattribute detectors.
 9. The method of claim 1, wherein the set ofmultiple attribute detectors comprises one or more attribute detectorsfor at least one of presence of a beard, presence of a moustache,presence of eyeglasses, presence of sunglasses, presence of a hat,absence of hair, shape of body features, shape of clothing features,clothing color and clothing texture.
 10. The method of claim 1, whereintracking each of the one or more individuals in a local field of view ineach of the two or more cameras comprises using one or more computervision algorithms.
 11. The method of claim 1, wherein the multipleattribute detectors are learned from a set of one or more trainingimages in one or more levels of resolution.
 12. The method of claim 1,wherein using the set of multiple attribute detectors to match anindividual tracked in one of the two or more cameras with an individualtracked in one or more other cameras of the two or more camerascomprises using at least one of a geometric configuration of the two ormore cameras, time information, and a set of individual attributes. 13.The method of claim 1, further comprising using one or more non-visualattributes from one or more sensors.
 14. The method of claim 13, whereinthe one or more non-visual attributes comprises at least one of odor andtemperature.
 15. The method of claim 1, further comprising providing asystem, wherein the system comprises one or more distinct softwaremodules, each of the one or more distinct software modules beingembodied on a tangible computer-readable recordable storage medium, andwherein the one or more distinct software modules comprise a persondetection module, a person tracking module, a human parsing module and amatching module executing on a hardware processor.
 16. A computerprogram product comprising a tangible computer readable recordablestorage device including computer useable program code for tracking anindividual across two or more cameras, the computer program productincluding: computer useable program code for detecting an image of oneor more individuals in each of two or more cameras; computer useableprogram code for tracking each of the one or more individuals in a fieldof view in each of the two or more cameras; computer useable programcode for applying a set of multiple attribute detectors to the images ofeach of the one or more individuals being tracked by the two or morecameras, wherein said attribute detectors (i) provide a probability thatgiven attributes are present in an image and (ii) are learned fromtraining images in multiple levels of resolution to produce robustnessto multiple changes in lighting and viewpoint; and computer useableprogram code for using the set of multiple attribute detectors to matchan individual tracked in one of the two or more cameras with anindividual tracked in one or more other cameras of the two or morecameras, wherein the computer useable program code for using the set ofmultiple attribute detectors to match an individual tracked in one ofthe two or more cameras with an individual tracked in one or more othercameras of the two or more cameras comprises: computer useable programcode for using a maximum confidence value of each of the multipleattribute detectors to generate a feature vector of each of the one ormore individuals; computer useable program code for determining a weightassociated with each of the multiple attribute detectors, wherein saidweight corresponds to a reliability measure of the correspondingattribute detector relative to the remaining attribute detectors in theset of multiple attribute detectors; computer useable program code forcalculating a distance between each vector using a weighted vectordistance between each vector based on the determined weight associatedwith each of the multiple attribute detectors; and computer useableprogram code for comparing the distance to a threshold to determine ifthe individual tracked in one of the two or more cameras is the sameindividual as the individual tracked in one or more other cameras of thetwo or more cameras.
 17. The computer program product of claim 16,further comprising computer useable program code for assigning a uniquetracking identifier for each trajectory of an individual locally trackedin each of the two or more cameras to facilitate tracking in a globalcoordinate system.
 18. The computer program product of claim 16, furthercomprising computer useable program code for wherein the computeruseable program code for using the set of multiple attribute detectorsto match an individual tracked in one of the two or more cameras with anindividual tracked in one or more other cameras of the two or morecameras comprises computer useable program code for using at least oneof a geometric configuration of the two or more cameras, timeinformation, and a set of individual attributes.
 19. The computerprogram product of claim 16, further comprising: computer useableprogram code for obtaining a feature vector for the individual when theindividual moves within the field of view of a second camera; andcomputer useable program code for determining an amount of time taken bythe individual to leave a view of the first camera and appear within aview of the second camera to determine if the amount of time isconsistent with an established range of time.
 20. A system for trackingan individual across two or more cameras, comprising: a memory; and atleast one processor coupled to the memory and operative to: detect animage of one or more individuals in each of two or more cameras; trackeach of the one or more individuals in a field of view in each of thetwo or more cameras; apply a set of multiple attribute detectors to theimages of each of the one or more individuals being tracked by the twoor more cameras, wherein said attribute detectors (i) provide aprobability that given attributes are present in an image and (ii) arelearned from training images in multiple levels of resolution to producerobustness to multiple changes in lighting and viewpoint; and using theset of multiple attribute detectors to match an individual tracked inone of the two or more cameras with an individual tracked in one or moreother cameras of the two or more cameras, wherein at least one processorcoupled to the memory and operative to using the set of multipleattribute detectors to match an individual tracked in one of the two ormore cameras with an individual tracked in one or more other cameras ofthe two or more cameras is further operative to: use a maximumconfidence value of each of the multiple attribute detectors to generatea feature vector of each of the one or more individuals; determine aweight associated with each of the multiple attribute detectors, whereinsaid weight corresponds to a reliability measure of the correspondingattribute detector relative to the remaining attribute detectors in theset of multiple attribute detectors; calculate a distance between eachvector using a weighted vector distance between each vector based on thedetermined weight associated with each of the multiple attributedetectors; and compare the distance to a threshold to determine if theindividual tracked in one of the two or more cameras is the sameindividual as the individual tracked in one or more other cameras of thetwo or more cameras.
 21. The system of claim 20, wherein the at leastone processor coupled to the memory is further operative to assign aunique tracking identifier for each trajectory of an individual locallytracked in each of the two or more cameras to facilitate tracking in aglobal coordinate system.
 22. The system of claim 20, wherein the atleast one processor coupled to the memory is operative to use the set ofmultiple attribute detectors to match an individual tracked in one ofthe two or more cameras with an individual tracked in one or more othercameras of the two or more cameras is further operative to use at leastone of a geometric configuration of the two or more cameras, timeinformation, and a set of individual attributes.
 23. The system of claim20, wherein the at least one processor coupled to the memory is furtheroperative to: obtain a feature vector for the individual when theindividual moves within the field of view of a second camera; anddetermine an amount of time taken by the individual to leave a view ofthe first camera and appear within a view of the second camera todetermine if the amount of time is consistent with an established rangeof time.